Most existing ontology matching methods utilize the literal information to discover alignments. However, some literal information in ontologies may be opaque and some ontologies may not have sufficient literal information. In this paper, these ontologies are named as weak informative ontologies (WIOs) and it is challenging for existing methods to matching WIOs. On one hand, string-based and linguistic-based matching methods cannot work well for WIOs. On the other hand, some matching methods use external resources to improve their performance, but collecting and processing external resources is still time-consuming. To address this issue, this paper proposes a practical method for matching WIOs by employing the ontology structure information to discover alignments. First, the semantic subgraphs are extracted from the ontology graph to capture the precise meanings of ontology elements. Then, a new similarity propagation model is designed for matching WIOs. Meanwhile, in order to avoid meaningless propagation, the similarity propagation is constrained by semantic subgraphs and other conditions. Consequently, the similarity propagation model ensures a balance between efficiency and quality during matching. Finally, the similarity propagation model uses a few credible alignments as seeds to find more alignments, and some useful strategies are adopted to improve the performance. This matching method for WIOs has been implemented in the ontology matching system Lily. Experimental results on public OAEI benchmark datasets demonstrate that Lily significantly outperforms most of the state-of-the-art works in both WIO matching tasks and general ontology matching tasks. In particular, Lily increases the recall by a large margin, while it still obtains high precision of matching results.

翻译：现有本体匹配方法大多利用文字信息发现对齐关系。然而，本体中的部分文字信息可能具有模糊性，或某些本体缺乏足够的文字信息。本文将这类本体定义为弱信息本体（WIOs），现有方法在匹配WIOs时面临挑战：一方面，基于字符串和语言学的匹配方法难以有效处理WIOs；另一方面，部分方法借助外部资源提升性能，但外部资源的收集与处理仍耗时费力。为解决该问题，本文提出一种利用本体结构信息发现对齐关系的实用WIOs匹配方法。首先，从本体图中提取语义子图以捕获本体元素的精确含义；其次，设计新型相似度传播模型用于匹配WIOs；同时，为避免无效传播，通过语义子图及其他条件对相似度传播进行约束，从而在匹配过程中平衡效率与质量；最后，该相似度传播模型以少量可靠对齐作为种子发现更多对齐，并采用若干有效策略提升性能。该WIOs匹配方法已在本体匹配系统Lily中实现。在公开OAEI基准数据集上的实验表明，Lily在WIO匹配任务和通用本体匹配任务中均显著优于大多数现有先进方法。特别地，Lily在保持高精度匹配结果的同时，大幅提升了召回率。